This model simulates the propagation of photons in a water tank. A source of light emits an impulse of photons with equal energy represented by yellow dots. These photons are then scattered by water particles before possibly reaching the photo-detector represented by a gray line. Different types of water are considered. For each one of them we calculate the total received energy.

The water tank is represented by a blue rectangle with fixed dimensions. It’s exposed to the air interface and has totally absorbent barriers. Four types of water are supported. Each one is characterized by its absorption and scattering coefficients.
At the source, the photons are generated uniformly with a random direction within the beamwidth. Each photon travels a random distance drawn from a distribution depending on the water characteristics before encountering a water particle.
Based on the updated position of the photon, three situations may occur:
-The photon hits the barrier of the tank on its trajectory. In this case it’s considered as lost since the barriers are assumed totally absorbent.
…

Netlogo model used to produce the results in my doctoral thesis

ReMoTe-S is an agent-based model of the residential mobility of Swiss tenants. Its goal is to foster a holistic understanding of the reciprocal influence between households and dwellings and thereby inform a sustainable management of the housing stock. The model is based on assumptions derived from empirical research conducted with three housing providers in Switzerland and can be used mainly for two purposes: (i) the exploration of what if scenarios that target a reduction of the housing footprint while accounting for households’ preferences and needs; (ii) knowledge production in the field of residential mobility and more specifically on the role of housing functions as orchestrators of the relocation process.

This is an agent-based model with two types of agents: customers and insurers. Insurers are price-takers who choose how much to spend on their service quality, and customers evaluate insurers based on premium, brand preference, and their perceived service quality. Customers are also connected in a small-world network and may share their opinions with their network.

The ABM contains two types of agents: insurers and customers. These act within the environment of a motor insurance market. At each simulation, the model undergoes the following steps:

1. Network generation: At the start of the simulation, the model generates a small world network of social links between the customers, and randomly assigns each customer to an initial insurer
...

In this model, the spread of a virus disease in a network consisting of school pupils, employed, and umemployed people is simulated. The special feature in this model is the distinction between different types of links: family-, friends-, school-, or work-links. In this way, different governmental measures can be implemented in order to decelerate or stop the transmission.

Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).

The Agent-Based Wildfire Simulation Environment (ABWiSE) translates the concept of a moving fire front as a set of mobile fire agents that respond to, and interact with, vegetation, wind, and terrain. Presently, the purpose of ABWiSE is to explore how ABM, using simple interactions between agents and a simple atmospheric feedback model, can simulate emergent fire spread patterns.

Substitution of food products will be key to realising widespread adoption of sustainable diets. We present an agent-based model of decision-making and influences on food choice, and apply it to historically observed trends of British whole and skimmed (including semi) milk consumption from 1974 to 2005. We aim to give a plausible representation of milk choice substitution, and test different mechanisms of choice consideration. Agents are consumers that perceive information regarding the two milk choices, and hold values that inform their position on the health and environmental impact of those choices. Habit, social influence and post-decision evaluation are modelled. Representative survey data on human values and long-running public concerns empirically inform the model. An experiment was run to compare two model variants by how they perform in reproducing these trends. This was measured by recording mean weekly milk consumption per person. The variants differed in how agents became disposed to consider alternative milk choices. One followed a threshold approach, the other was probability based. All other model aspects remained unchanged. An optimisation exercise via an evolutionary algorithm was used to calibrate the model variants independently to observed data. Following calibration, uncertainty and global variance-based temporal sensitivity analysis were conducted. Both model variants were able to reproduce the general pattern of historical milk consumption, however, the probability-based approach gave a closer fit to the observed data, but over a wider range of uncertainty. This responds to, and further highlights, the need for research that looks at, and compares, different models of human decision-making in agent-based and simulation models. This study is the first to present an agent-based modelling of food choice substitution in the context of British milk consumption. It can serve as a valuable pre-curser to the modelling of dietary shift and sustainable product substitution to plant-based alternatives in Britain.

This is an empirical model described in http://dx.doi.org/10.1016/j.landurbplan.2010.05.001. The objective of the model is to simulate how the decision-making of farmers/agents with different strategies can affect the landscape structure in a region in the Netherlands.

A simulated approach for Personal Carbon Trading, for figuring out what effects it might have if it will be implemented in the real world. We use an artificial population with some empirical data from international literature and basic assumptions about heterogeneous energy demand. The model is not to be used as simulating the actual behavior of real populations, but a toy model to test the effects of differences in various factors such as number of agents, energy price, price of allowances, etc. It is important to adapt the model for specific countries as carbon footprint and energy demand determines the relative success of PCT.